Many organizations struggle to expand AI initiatives from small pilot projects to full-scale enterprise solutions. While testing generative models has become common, actually integrating these tools into everyday business operations often hits roadblocks. The challenge lies in turning AI investments into real, operational value that benefits the entire company.

Rethinking AI Construction with Asset-Based Consulting

Traditional consulting firms usually rely on human labor to solve integration issues, which can be slow and costly. To change this, IBM is offering an asset-based consulting approach. Instead of building custom solutions for each client, IBM combines expert advice with a library of ready-made software assets. This helps organizations quickly assemble and manage their own AI platforms.

Instead of creating new systems from scratch, businesses can use existing architectures to streamline processes and connect AI tools to older systems. This method allows companies to scale new AI applications without needing to overhaul their core infrastructure, models, or cloud services. It simplifies the process and reduces costs, making AI more accessible and practical for large organizations.

Supporting Multi-Cloud Environments and Reducing Lock-In

One common concern for companies is vendor lock-in, especially when adopting proprietary platforms. IBM’s approach recognizes that most large enterprises operate in a multi-cloud world. Their services support a variety of cloud providers, including Amazon Web Services, Google Cloud, and Microsoft Azure, along with IBM’s own watsonx platform.

This flexibility means organizations don’t have to abandon their current investments or switch ecosystems entirely. They can build on existing tools and models—whether open-source or proprietary—without fearing the technical debt that often comes with changing platforms. This approach lowers the barrier to adopting AI at scale and encourages ongoing innovation.

The backbone of this offering is IBM Consulting Advantage, IBM’s internal delivery platform. Having used it in over 150 client projects, IBM reports that it has boosted their consultants’ productivity by up to 50 percent. If these tools can help IBM work faster, they are likely to do the same for other companies, accelerating AI deployment and value creation.

Building Ecosystems with AI Marketplaces

The service also includes access to a marketplace of industry-specific AI agents and applications. This marketplace makes it easier for organizations to find and implement solutions tailored to their needs. Instead of managing individual models, businesses can focus on overseeing a cohesive ecosystem of digital and human workers working together seamlessly.

Adopting a platform-centric approach shifts the focus from individual AI models to managing a complete digital environment. This strategy helps companies scale their AI efforts more effectively, ensuring that new tools integrate smoothly with existing workflows and infrastructure.

Real-world examples show this approach working in action. For instance, Pearson, a global learning company, is using this service to build a custom AI platform tailored to their needs. Their experience demonstrates how active deployment of such platforms can enhance operational efficiency and unlock new value from AI investments.

Overall, moving beyond pilot projects by adopting a platform-focused, asset-based strategy allows organizations to realize the full potential of AI. It reduces complexity, minimizes risks, and accelerates the path to enterprise-wide AI adoption, making it a practical approach for modern businesses looking to stay competitive in a digital world.

Inspired by

• https://www.artificialintelligence-news.com/news/scaling-ai-value-beyond-pilot-phase-purgatory/

Sources

• ibm.com